1. Field of the Invention
The present invention generally relates to methods of underwriting profitability analysis and, more particularly, to a computer implemented process that applies data mining techniques to historical policy and claims for extracting rules that describe policy holders and uses these rules to classify policy holders into distinct risk groups.
2. Background Description
The first problem addressed by this invention is the robust and rigorous estimation of pure premiums for Property and Casualty (P&C) insurance books of business. These could be ongoing books of business, or new products under consideration. P&C insurance businesses need to constantly evaluate their existing and proposed books of business to identify potential issues and problems with populations they cover. Essentially, associated with each book of business is an insurance product which covers a (potentially vast) group of policy holders that have subscribed to the product. Inevitably, some policy holders file for claims. Associated with claims is their frequency (the rate at which they occur) and their severity (the amount at which they are settled). Insurance companies need to ensure that the premiums being charged are adequate to cover the claims being paid out, and the required profit margin. One approach to ensure this is to estimate the "pure premium" of each policy holder (the premium at which their expected claims payout equals premium charged). If the average pure premium across en entire book of business is equal to or less than the actual premium that goes with the product, then the business is essentially operating at a safe level.
The second problem addressed by this invention is the efficient delivery of the analytic process to a wide cross section of insurance decision makers in the actuarial, marketing, and underwriting areas. These functions could potentially be very disconnected in a firm, yet require a common set of solutions for the first problem detailed above.